Independent evaluation of a multi-view multi-task convolutional neural network breast cancer classification model using Finnish mammography screening data.

BACKGROUND: Development of deep convolutional neural networks for breast cancer classification has taken significant steps towards clinical adoption. It is though unclear how the models perform for unseen data, and what is required to adapt them to different demographic populations. In this retrospective study, we adopt an openly available pre-trained mammography breast cancer multi-view classification model and evaluate it by utilizing an independent Finnish dataset. METHODS: Transfer learning was used, and the pre-trained model was finetuned with 8,829 examinations from the Finnish dataset (4,321 normal, 362 malignant and 4,146 benign examinations). Holdout dataset with 2,208 examinations from the Finnish dataset (1,082 normal, 70 malignant and 1,056 benign examinations) was used in the evaluation. The performance was also evaluated on a manually annotated malignant suspect subset. Receiver Operating Characteristic (ROC) and Precision-Recall curves were used to performance measures. RESULTS: The Area Under ROC [95%CI] values for malignancy classification obtained with the finetuned model for the entire holdout set were 0.82 [0.76, 0.87], 0.84 [0.77, 0.89], 0.85 [0.79, 0.90], and 0.83 [0.76, 0.89] for R-MLO, L-MLO, R-CC and L-CC views respectively. Performance on the malignant suspect subset was slightly better. On the auxiliary benign classification task performance remained low. CONCLUSIONS: The results indicate that the model performs well also in an out-of-distribution setting. Finetuning allowed the model to adapt to some of the underlying local demographics. Future research should concentrate to identify breast cancer subgroups adversely affecting performance, as it is a requirement for increasing the model's readiness level for a clinical setting.

The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images.

OBJECTIVES: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. DESIGN: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). RESULTS: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57-0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52-0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. CONCLUSION: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.

Quantitative evaluation of the tibiofemoral joint cartilage by T2 mapping in patients with acute anterior cruciate ligament injury vs contralateral knees: results from the subacute phase using data from the NACOX study cohort.

OBJECTIVE: Immediate cartilage structural alterations in the acute phase after an anterior cruciate ligament (ACL) rupture may be a precursor to posttraumatic osteoarthritis (PTOA) development. Our aim was to describe changes in cartilage matrix in the subacute phase of the acutely ACL-injured knee compared to the contralateral uninjured knee. DESIGN: Participants (n = 118) aged 15-40 years with an acute ACL injury were consecutively included in subacute phase after acute ACL-injury and underwent MRI (mean 29 days post trauma) of both knees. Mean T2 relaxation times, T2 spatial coefficient of variation and cartilage thickness were determined for different regions of the tibiofemoral cartilage. Differences between the acutely ACL-injured and uninjured knee were evaluated using Wilcoxon signed-rank test. RESULTS: T2 relaxation time in injured knees was increased in multiple cartilage regions from both medial and lateral compartment compared to contralateral knees, mostly in medial trochlea and posterior tibia (P-value<0.001). In the same sites of injured knees, we observed significantly thinner cartilage. Moreover, injured knees presented shorter T2 relaxation time in superficial cartilage on lateral central femur and trochlea (P-value<0.001), and decreased T2 spatial coefficient of variation in lateral trochlea and load bearing regions of medial-central femoral condyle and central tibia in both compartments. CONCLUSION: Small but statistically significant differences were observed in the subacute phase between ACL-injured and uninjured knee in cartilage T2 relaxation time and cartilage thickness. Future longitudinal observations of the same cohort will allow for better understanding of early development of PTOA. TRIAL REGISTRATION NUMBER: NCT02931084.

The use of deep learning towards dose optimization in low-dose computed tomography: A scoping review.

INTRODUCTION: Low-dose computed tomography tends to produce lower image quality than normal dose computed tomography (CT) although it can help to reduce radiation hazards of CT scanning. Research has shown that Artificial Intelligence (AI) technologies, especially deep learning can help enhance the image quality of low-dose CT by denoising images. This scoping review aims to create an overview on how AI technologies, especially deep learning, can be used in dose optimisation for low-dose CT. METHODS: Literature searches of ProQuest, PubMed, Cinahl, ScienceDirect, EbscoHost Ebook Collection and Ovid were carried out to find research articles published between the years 2015 and 2020. In addition, manual search was conducted in SweMed+, SwePub, NORA, Taylor & Francis Online and Medic. RESULTS: Following a systematic search process, the review comprised of 16 articles. Articles were organised according to the effects of the deep learning networks, e.g. image noise reduction, image restoration. Deep learning can be used in multiple ways to facilitate dose optimisation in low-dose CT. Most articles discuss image noise reduction in low-dose CT. CONCLUSION: Deep learning can be used in the optimisation of patients' radiation dose. Nevertheless, the image quality is normally lower in low-dose CT (LDCT) than in regular-dose CT scans because of smaller radiation doses. With the help of deep learning, the image quality can be improved to equate the regular-dose computed tomography image quality. IMPLICATIONS TO PRACTICE: Lower dose may decrease patients' radiation risk but may affect the image quality of CT scans. Artificial intelligence technologies can be used to improve image quality in low-dose CT scans. Radiologists and radiographers should have proper education and knowledge about the techniques used.

Automated detection of patellofemoral osteoarthritis from knee lateral view radiographs using deep learning: data from the Multicenter Osteoarthritis Study (MOST).

OBJECTIVE: To assess the ability of imaging-based deep learning to detect radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. DESIGN: Knee lateral view radiographs were extracted from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 18,436 knees). Patellar region-of-interest (ROI) was first automatically detected, and subsequently, end-to-end deep convolutional neural networks (CNNs) were trained and validated to detect the status of patellofemoral OA. Patellar ROI was detected using deep-learning-based object detection method. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of classification models was assessed using the area under the receiver operating characteristic curve (ROC AUC) and the average precision (AP) obtained from the Precision-Recall (PR) curve in the stratified 5-fold cross validation setting. RESULTS: Of the 18,436 knees, 3,425 (19%) had PFOA. AUC and AP for the reference model including age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren-Lawrence (KL) grade to detect PFOA were 0.806 and 0.478, respectively. The CNN model that used only image data significantly improved the classifier performance (ROC AUC = 0.958, AP = 0.862). CONCLUSION: We present the first machine learning based automatic PFOA detection method. Furthermore, our deep learning based model trained on patella region from knee lateral view radiographs performs better at detecting PFOA than models based on patient characteristics and clinical assessments.

Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study.

OBJECTIVE: To introduce local binary pattern (LBP) texture analysis to cartilage osteoarthritis (OA) research and compare the performance of different classification systems in discrimination of OA subjects from healthy controls using gray-level co-occurrence matrix (GLCM) and LBP texture data. Classification algorithms were used to reduce the dimensionality of texture data into a likelihood of subject belonging to the reference class. METHOD: T2 relaxation time mapping with multi-slice multi-echo spin echo sequence was performed for eighty symptomatic OA patients and 63 asymptomatic controls on a 3T clinical MRI scanner. Relaxation time maps were subjected to GLCM and LBP texture analysis, and classification algorithms were deployed with an in-house developed software. Implemented algorithms were K nearest neighbors, support vector machine, and neural network classifier. RESULTS: LBP and GLCM discerned OA patients from controls with a significant difference in all studied regions. Classification models comprising GLCM and LBP showed high accuracy in classing OA patients and controls. The best performance was obtained with a multilayer perceptron type classifier with an overall accuracy of 90.2 %. CONCLUSION: LBP texture analysis complements prior results with GLCM, and together LBP and GLCM serve as significant input data for classification algorithms trained for OA assessment. Presented algorithms are adaptable to versatile OA evaluations also for future gradational or predictive approaches.

Patients', radiographers' and radiography students' experiences of 360° virtual counselling environment for the coronary computed tomography angiography: A qualitative study.

INTRODUCTION: The aim of this study was to describe patients', radiographers' and radiography students' experiences of the developed 360° virtual counselling environment (360°VCE) for the coronary computed tomography angiography (cCTA). METHODS: A descriptive qualitative approach was used. The participants were cCTA patients (n = 10), radiographers (n = 10) and radiography students (n = 10) who used the 360°VCE and visited or worked at a university hospital in Finland. The 360°VCE, resembling the authentic environments of a CT imaging unit, included digital counselling materials in text, image, animation and video formats. Data were gathered through thematic interviews individually to obtain an understanding of participants' perspectives and analyzed by inductive content analysis. RESULTS: Five main categories and 15 categories were identified. Identified benefits of the 360°VCE for patients included improvements in knowledge, spatial and environmental orientation, and senses of security and self-efficacy, with reductions in fear and nervousness. Patients found the counselling materials engaging, and that the 360° technology conveniently provided reassuring familiarity with the environment before their visit. Identified benefits for radiographers and radiography students included improvements in patients' mental preparedness, knowledge, spatial and environmental orientation, and reductions in patients' fear, which eased procedures and enhanced diagnostic success. The 360°VCE also provided useful information and familiarization with the cCTA unit for students during clinical practice and staff of referring units.  CONCLUSION: It seems that patients', radiographers' and radiography students' experiences of 360°VCE respond to patients' needs by improved knowledge and reduced fears. Thus, current counselling practices can be usefully complemented with spherical panoramic imaging technology and online information delivery. IMPLICATIONS FOR PRACTICE: The results may be used to improve patient counselling and care, thereby optimizing the cCTA examination procedure and reducing fear. However, further research is needed to characterize experiences of the 360° VCE more comprehensively.

Low anti-Müllerian hormone level is not a risk factor for early pregnancy loss in IVF/ICSI treatment.

STUDY QUESTION: Is a low (<1.0 µg/L) or moderately low (1.0-1.9 µg/L) serum anti-Müllerian hormone (AMH) level a risk factor for early pregnancy loss in IVF/ICSI with a fresh or frozen-thawed embryo transfer (ET)? SUMMARY ANSWER: A low or moderately low serum AMH level does not associate with miscarriage, non-visualized pregnancy loss or overall early pregnancy loss rate in the IVF/ICSI treatment. WHAT IS KNOWN ALREADY: Low AMH predicts poor ovarian response and small oocyte yield in IVF/ICSI treatment, but its value in the evaluation of live birth rate (LBR) is modest. Little is known about the risk of early pregnancy loss in ART among women with low AMH. STUDY DESIGN, SIZE, DURATION: A retrospective cohort study on 1383 women undergoing their first oocyte retrieval for IVF/ICSI in Helsinki University Hospital in Helsinki, Finland, between 2012 and 2016, with all associated fresh (n = 1315) and frozen-thawed (n = 1418) ET cycles finished by August 2018. AMH was measured within 12 months before the IVF/ICSI stimulation. PARTICIPANTS/MATERIALS, SETTING, METHODS: Of all the women, 235 (17.0%) had low (<1.0 µg/L), 278 (20.1%) had moderately low (1.0-1.9 µg/L) and 870 (62.9%) had normal (≥2.0 µg/L) AMH. The primary outcomes were miscarriage, non-visualized pregnancy loss and early pregnancy loss (miscarriage and non-visualized pregnancy loss combined) after fresh or frozen-thawed ET. The impact of AMH on these outcomes was calculated in three populations: among all women who became pregnant, among women with AMH ≤6.0 µg/L and in a population weighted by the inverse probability of becoming pregnant (inverse probability weighting, IPW). The impact of AMH was also assessed on the secondary outcomes, cumulative pregnancy rate (cPR) and cumulative live birth rate (cLBR) across all ET cycles in the woman's first IVF/ICSI. Potential confounders (the woman's age, overweight, smoking, history of endometriosis and underlying medical conditions) adjusted the final results. MAIN RESULTS AND THE ROLE OF CHANCE: Of 1123 pregnancies, 285 (25.4%) ended in non-visualized pregnancy loss and 143 (12.7%) in miscarriage. The LBR was 24.6% per ET (673/2733). Low or moderately low AMH, compared with normal AMH, did not associate with miscarriage or non-visualized pregnancy loss in analyses among all women who became pregnant (adjusted relative risk (RR) for miscarriage vs live birth, 0.70 and 95% CI 0.42-1.17 in low AMH and adjusted RR, 1.00 and 95% CI, 0.68-1.49 in moderately low AMH; adjusted RR for non-visualized pregnancy loss vs live birth, 0.90 and 95% CI, 0.65-1.23 in low AMH and adjusted RR, 1.09 and 95% CI 0.85-1.41 in moderately low AMH), nor did low or moderately low AMH associate with the overall early pregnancy loss rate (adjusted RR for early pregnancy loss vs live birth, 0.86 and 95% CI, 0.68-1.10 in low AMH and adjusted RR, 1.01 and 95% CI, 0.86-1.27 in moderately low AMH). Results remained similar after restricting the analysis to women with AMH ≤6.0 µg/L. Women with low or moderately low AMH had fewer pregnancies and live births than women with normal AMH in their first IVF/ICSI (cPR/cLBR in women with low AMH 50.6/34.0%, moderately low AMH 59.0/36.3% and normal AMH 68.3/49.2%). When the lower probability for pregnancy was considered by using IPW, women with low or moderately low AMH did not have a higher risk for miscarriage, non-visualized pregnancy loss or overall early pregnancy loss compared with women with normal AMH. LIMITATIONS, REASONS FOR CAUTION: The number of miscarriages in women with low AMH was moderately small, limiting the power of the study. The real-world clinical setting of the study restricted the ability to control for all factors causing selection bias. WIDER IMPLICATIONS OF THE FINDINGS: The cLBR was higher among women with normal AMH than among women with low or moderately low AMH in their first IVF/ICSI treatment because these women had more oocytes and embryos. Women with low or moderately low AMH did not have an increased risk for early pregnancy loss. This information is reassuring for couples and useful in counseling. These results are also valuable when assessing the overall effectiveness of IVF/ICSI treatment. STUDY FUNDING/COMPETING INTEREST(S): Research funds from Helsinki University Hospital (no. TYH2018232), Hyvinkää Hospital (no. M3080TUT18) and the Emil Aaltonen Foundation for P.P. Grants from the Paulo Foundation and the Finnish Medical Foundation for H.H. The authors report no conflicts of interest. TRIAL REGISTRATION NUMBER: HUS/138/2017.

Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis.

OBJECTIVE: The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). DESIGN: Bilateral posterior-anterior knee radiographs were analyzed from the baseline of Osteoarthritis Initiative (OAI) (9012 knee radiographs) and Multicenter Osteoarthritis Study (MOST) (3,644 knee radiographs) datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. Subsequently, we built logistic regression models to identify and compare the performances of several texture descriptors and each ROI placement method using 5-fold cross validation. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset. We used area under the receiver operating characteristic curve (ROC AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. RESULTS: We found that the adaptive ROI improves the classification performance (OA vs non-OA) over the commonly-used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, Local Binary Pattern (LBP) yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. CONCLUSION: Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA.

Defective WNT signaling may protect from articular cartilage deterioration - a quantitative MRI study on subjects with a heterozygous WNT1 mutation.

OBJECTIVE: WNT signaling is of key importance in chondrogenesis and defective WNT signaling may contribute to the pathogenesis of osteoarthritis and other cartilage diseases. Biochemical composition of articular cartilage in patients with aberrant WNT signaling has not been studied. Our objective was to assess the knee articular cartilage in WNT1 mutation-positive individuals using a 3.0T MRI unit to measure cartilage thickness, relaxation times, and texture features. DESIGN: Cohort comprised mutation-positive (N = 13; age 17-76 years) and mutation-negative (N = 13; 16-77 years) subjects from two Finnish families with autosomal dominant WNT1 osteoporosis due to a heterozygous missense mutation c.652T>G (p.C218G) in WNT1. All subjects were imaged with a 3.0T MRI unit and assessed for cartilage thickness, T2 and T1ρ relaxation times, and T2 texture features contrast, dissimilarity and homogeneity of T2 relaxation time maps in six regions of interest (ROIs) in the tibiofemoral cartilage. RESULTS: All three texture features showed opposing trends with age between the groups in the medial tibiofemoral cartilage (P = 0.020-0.085 for the difference of the regression coefficients), the mutation-positive individuals showing signs of cartilage preservation. No significant differences were observed in the lateral tibiofemoral cartilage. Cartilage thickness and means of T2 relaxation time did not differ between groups. Means of T1ρ relaxation time were significantly different in one ROI but the regression analysis displayed no differences. CONCLUSIONS: Our results show less age-related cartilage deterioration in the WNT1 mutation-positive than the mutation-negative subjects. This suggests, that the WNT1 mutation may alter cartilage turnover and even have a potential cartilage-preserving effect.

Osteoarthritis year in review 2018: imaging.

PURPOSE: To provide a narrative review of the most relevant original research published in 2017/2018 on osteoarthritis imaging. METHODS: The PubMed database was used to recover all relevant articles pertaining to osteoarthritis and medical imaging published between 1 April 2017 and 31 March 2018. Review articles, case studies and in vitro or animal studies were excluded. The original publications were subjectively sorted based on relevance, novelty and impact. RESULTS AND CONCLUSIONS: The publication search yielded 1,155 references. In the assessed publications, the most common imaging modalities were radiography (N = 708) and magnetic resonance imaging (MRI) (355), followed by computed tomography (CT) (220), ultrasound (85) and nuclear medicine (17). An overview of the most important publications to the osteoarthritis (OA) research community is presented in this narrative review. Imaging studies play an increasingly important role in OA research, and have helped us to understand better the pathophysiology of OA. Radiography and MRI continue to be the most applied imaging modalities, while quantitative MRI methods and texture analysis are becoming more popular. The value of ultrasound in OA research has been demonstrated. Several multi-modality predictive models have been developed. Deep learning has potential for more automatic and standardized analyses in future OA imaging research.

Causes for delay before specialist consultation in head and neck cancer.

BACKGROUND: Head and neck cancers are often diagnosed at a late stage, thus resulting in a generally poor prognosis. This is partly attributable to patients' hesitancy in seeking treatment. However, the length and causes of these patient delays remain relatively unknown. MATERIAL AND METHODS: We included all new head and neck cancer patients treated at our tertiary care center between 2016 and 2017. Using a patient questionnaire, we collected data on patients' symptoms and other factors related to seeking medical care, and recorded both patient- and primary health care-related delays. We then compared the data collected from these patients to patient and tumor characteristics collected from hospital records, and analyzed various causes for delay before a specialist consultation to the Department of Otorhinolaryngology - Head and Neck Surgery. RESULTS: Among the patients (n = 142) in our study, the median patient delay was 35 d with 73% of patients seeking medical care within 3 months. In comparison, the median primary health-care delay was 20 d. Certain symptoms influenced patient delay. Hoarseness and breathing difficulties correlated with longer patient delay while patients with a lump on the neck had a shorter delay. Patient delay was associated with certain tumor-related factors such as the tumor site and the presence of regional metastases, which resulted in shorter patient delay. None of the patient-related factors appeared to impact delay. Important factors influencing primary health-care delay included the initial location visited and whether any follow-up visit was scheduled or not. CONCLUSIONS: Although most patients sought medical advice without a major delay and were adequately referred, we found that long delays existed. Raising awareness of the symptoms of head and neck cancer among general population and health-care providers is probably the best way to get patients to curative treatment without delay.

Metabolic syndrome associates with left atrial dysfunction.

BACKGROUND AND AIMS: Obesity and metabolic syndrome (MetS) are risk factors of atrial fibrillation (AF), but limited data exist on their effect on left atrial (LA) function. The aim of the study was to evaluate the effects of cardiac, hepatic and intra-abdominal ectopic fat depots and cardiometabolic risk factors on LA function in non-diabetic male subjects. METHODS AND RESULTS: Myocardial and hepatic triglyceride contents were measured with 1.5T 1H-magnetic resonance spectroscopy and LA and left ventricular function, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), epicardial and pericardial fat by magnetic resonance imaging (MRI) in 33 men with MetS and 40 men without MetS. LA volumes were assessed using a novel three-chamber orientation based MRI approach. LA ejection fraction (EF) was lower in MetS patients than in the control group (44 ± 7.7% in MetS vs. 49 ± 8.6% in controls, p = 0.013) without LA enlargement, indicating LA dysfunction. LA EF correlated negatively with waist circumference, body mass index, SAT, VAT, fasting serum insulin, and homeostasis model assessment of insulin resistance index, and positively with fasting serum high-density lipoprotein cholesterol. VAT was the best predictor of reduced LA EF. CONCLUSIONS: MetS associates with subclinical LA dysfunction. Multiple components of MetS are related to LA dysfunction, notably visceral obesity and insulin resistance. Further studies are needed to elucidate the role of mechanical atrial remodeling in the development of AF.

Assessment of meniscus with adiabatic T1ρ and T2ρ relaxation time in asymptomatic subjects and patients with mild osteoarthritis: a feasibility study.

OBJECTIVE: To investigate the ability of magnetic resonance imaging (MRI) adiabatic relaxation times in the rotating frame (adiabatic T1ρ and T2ρ) to detect structural alterations in meniscus tissue of mild OA patients and asymptomatic volunteers. METHOD: MR images of 24 subjects (age range: 50-67 years, 12 male), including 12 patients with mild osteoarthritis (OA) (Kellgren-Lawrence (KL) = 1, 2) and 12 asymptomatic volunteers, were acquired using a 3 T clinical MRI system. Morphological assessment was performed using semiquantitative MRI OA Knee Score (MOAKS). Adiabatic T1ρ and T2ρ (AdT1ρ, AdT2ρ) relaxation time maps were calculated in regions of interest (ROIs) containing medial and lateral horns of menisci. The median relaxation time values of the ROIs were compared between subjects classified based on radiographic findings and MOAKS evaluations. RESULTS: MOAKS assessment of patients and volunteers indicated the presence of meniscal and cartilage lesions in both groups. For the combined cohort group, prolonged AdT1ρ was observed in the posterior horn of the medial meniscus (PHMED) in subjects with MOAKS meniscal tear (P < 0.05). AdT2ρ was statistically significantly longer in PHMED of subjects with MOAKS full-thickness cartilage loss (P < 0.05). After adjusting for multiple comparisons, differences in medians of observed AdT1ρ and AdT2ρ values between mild OA patients and asymptomatic volunteers did not reach statistical significance. CONCLUSION: AdT1ρ and AdT2ρ measurements have the potential to identify changes in structural composition of meniscus tissue associated with meniscal tear and cartilage loss in a cohort group of mild OA patients and asymptomatic volunteers.

Association between radiography-based subchondral bone structure and MRI-based cartilage composition in postmenopausal women with mild osteoarthritis.

OBJECTIVE: Our aim was to investigate the relation between radiograph-based subchondral bone structure and cartilage composition assessed with delayed gadolinium enhanced magnetic resonance imaging of cartilage (dGEMRIC) and T2 relaxation time. DESIGN: Ninety-three postmenopausal women (Kellgren-Lawrence grade 0: n = 13, 1: n = 26, 2: n = 54) were included. Radiograph-based bone structure was assessed using entropy of the Laplacian-based image (ELap) and local binary patterns (ELBP), homogeneity indices of the local angles (HIAngles,mean, HIAngles,Perp, HIAngles,Paral), and horizontal (FDHor) and vertical fractal dimensions (FDVer). Mean dGEMRIC index and T2 relaxation time of tibial cartilage were calculated to estimate cartilage composition. RESULTS: HIAngles,mean (rs = -0.22) and HIAngles,Paral (rs = -0.24) in medial subchondral bone were related (P < 0.05) to dGEMRIC index of the medial tibial cartilage. ELap (rs = -0.23), FDHor,0.34 mm (r = 0.21) and FDVer,0.68 mm (r = 0.24) in medial subchondral bone were related (P < 0.05) to T2 relaxation time values of the medial tibial cartilage. FDHor at different scales in lateral subchondral bone were related (P < 0.01) to dGEMRIC index (r = 0.29-0.41) and T2 values of lateral tibial cartilage (r = -0.28 to -0.36). FDVer at larger scales were related (P < 0.05) to dGEMRIC index (r = 0.24-0.25) and T2 values of lateral tibial cartilage (r = -0.21). HIAngles,Paral (r = -0.25) and FDVer,0.68 mm (rs = 0.22) in the lateral tibial trabecular bone were related (P < 0.05) to dGEMRIC index of the lateral tibial cartilage. CONCLUSION: Our results support the presumption that several tissues are affected in the early osteoarthritis (OA). Furthermore, they indicate that the detailed analysis of radiographs may serve as a complementary imaging tool for OA studies.

Magnetic resonance imaging (MRI)-defined cartilage degeneration and joint pain are associated with poor physical function in knee osteoarthritis - the Oulu Knee Osteoarthritis study.

OBJECTIVE: The main aim was to investigate the associations between Magnetic Resonance Imaging (MRI)-defined structural pathologies of the knee and physical function. DESIGN: A cohort study with frequency matching on age and sex with eighty symptomatic subjects with knee pain and suspicion or diagnosis of knee osteoarthritis (OA) and 57 asymptomatic subjects was conducted. The subjects underwent knee MRI, and the severity of structural changes was graded by MRI Osteoarthritis Knee Score (MOAKS) in separate knee locations. WOMAC function subscores were recorded and physical function tests (20-m and 5-min walk, stair ascending and descending, timed up & go and repeated sit-to-stand tests) performed. The association between MRI-defined structural pathologies and physical function tests and WOMAC function subscores were evaluated by linear regression analysis with adjustment for demographic factors, other MRI-features and pain with using effect size (ES) as a measure of the magnitude of an association. RESULTS: Cartilage degeneration showed significant association with poor physical performance in TUG-, stair ascending and descending-, 20-m- and 5-min walk-tests (ESs in the subjects with cartilage degeneration anywhere between 0.134 [95%CI 0.037-0.238] and 0.224 [0.013-0.335]) and with increased WOMAC function subscore (ES in the subjects with cartilage degeneration anywhere 0.088 [0.012-0.103]). Also, lateral meniscus maceration and extrusion were associated with poor performance in stair ascending test (ESs 0.067 [0.008-0.163] and 0.077 [0.012-0.177]). CONCLUSIONS: After adjustments cartilage degeneration was associated with both decreased self-reported physical function and poor performance in the physical function tests. Furthermore, subjects with lateral meniscus maceration and extrusions showed significantly worse performance in stair ascending tests.

Effects of high intensity resistance aquatic training on body composition and walking speed in women with mild knee osteoarthritis: a 4-month RCT with 12-month follow-up.

OBJECTIVE: To investigate the effects of 4-months intensive aquatic resistance training on body composition and walking speed in post-menopausal women with mild knee osteoarthritis (OA), immediately after intervention and after 12-months follow-up. Additionally, influence of leisure time physical activity (LTPA) will be investigated. DESIGN: This randomised clinical trial assigned eighty-seven volunteer postmenopausal women into two study arms. The intervention group (n = 43) participated in 48 supervised intensive aquatic resistance training sessions over 4-months while the control group (n = 44) maintained normal physical activity. Eighty four participants continued into the 12-months' follow-up period. Body composition was measured with dual-energy X-ray absorptiometry (DXA). Walking speed over 2 km and the knee injury and osteoarthritis outcome score (KOOS) were measured. LTPA was recorded with self-reported diaries. RESULTS: After the 4-month intervention there was a significant decrease (P = 0.002) in fat mass (mean change: -1.17 kg; 95% CI: -2.00 to -0.43) and increase (P = 0.002) in walking speed (0.052 m/s; 95% CI: 0.018 to 0.086) in favour of the intervention group. Body composition returned to baseline after 12-months. In contrast, increased walking speed was maintained (0.046 m/s; 95% CI 0.006 to 0.086, P = 0.032). No change was seen in lean mass or KOOS. Daily LTPA over the 16-months had a significant effect (P = 0.007) on fat mass loss (f2 = 0.05) but no effect on walking speed. CONCLUSIONS: Our findings show that high intensity aquatic resistance training decreases fat mass and improves walking speed in post-menopausal women with mild knee OA. Only improvements in walking speed were maintained at 12-months follow-up. Higher levels of LTPA were associated with fat mass loss. TRIAL REGISTRATION NUMBER: ISRCTN65346593.

THE ESTABLISHMENT OF LOCAL DIAGNOSTIC REFERENCE LEVELS IN ENDOSCOPIC RETROGRADE CHOLANGIOPANCREATOGRAPHY: A PRACTICAL TOOL FOR THE OPTIMISATION AND FOR QUALITY ASSURANCE MANAGEMENT.

Fluoroscopic procedures are an area of special concern in relation to radiation protection. The aim of this study was to describe the current level of patient radiation doses in endoscopic retrograde cholangiopancreatography (ERCP) collected from a single centre, as well as to establish and review local diagnostic reference levels (DRLs) in ERCP. A total of 100 patients' radiation doses in ERCP were recorded, and the third-quartile method was adopted to establish local DRLs for ERCP. The mean dose area product (DAP) was 2.05 Gy cm2, fluoroscopy time (FT) 1.7 min and the number of images was 3. The proposed local DRLs for ERCP were 3.00 Gy cm2 and 3.0 min. Local DRLs were reviewed in a sample of 25 patients 5 y after they had been established. In reviewing data, the averages of DAP and FT were below the local DRLs. Local DRLs help in the optimisation process of fluoroscopic procedures and guides to a good clinical practice.

Effect of progressive high-impact exercise on femoral neck structural strength in postmenopausal women with mild knee osteoarthritis: a 12-month RCT.

It is uncertain whether subjects with mild knee osteoarthritis, and who may be at risk of osteoporosis, can exercise safely with the aim of improving hip bone strength. This RCT showed that participating in a high-impact exercise program improved femoral neck strength without any detrimental effects on knee cartilage composition. INTRODUCTION: No previous studies have examined whether high-impact exercise can improve bone strength and articular cartilage quality in subjects with mild knee osteoarthritis. In this 12-month RCT, we assessed the effects of progressive high-impact exercise on femoral neck structural strength and biochemical composition of knee cartilage in postmenopausal women. METHODS: Eighty postmenopausal women with mild knee radiographic osteoarthritis were randomly assigned into the exercise (n = 40) or control (n = 40) group. Femoral neck structural strength was assessed with dual-energy X-ray absorptiometry. The knee cartilage region exposed to exercise loading was measured by the quantitative MRI techniques of T2 mapping and delayed gadolinium-enhanced MRI of cartilage (dGEMRIC). Also, an accelerometer-based body movement monitor was used to evaluate the total physical activity loading on the changes of femoral neck strength in all participants. Training effects on the outcome variables were estimated by the bootstrap analysis of covariance. RESULTS: A significant between-group difference in femoral neck bending strength in favor of the trainees was observed after the 12-month intervention (4.4%, p < 0.01). The change in femoral neck bending strength remained significant after adjusting for baseline value, age, height, and body mass (4.0%, p = 0.020). In all participants, the change in bending strength was associated with the total physical activity loading (r = 0.29, p = 0.012). The exercise participation had no effect on knee cartilage composition. CONCLUSION: The high-impact training increased femoral neck strength without having any harmful effect on knee cartilage in women with mild knee osteoarthritis. These findings imply that progressive high-impact exercise is a feasible method in seeking to prevent hip fractures in postmenopausal women whose articular cartilage may also be frail.